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High-dimensional data, characterized by many features, can be difficult to visualize effectively. Dimensionality reduction techniques, such as PCA, UMAP, and t-SNE, address this challenge by projecting the data into a lower-dimensional space while preserving important relationships. TopoMap is another technique that excels at preserving the underlying structure of the data, leading to interpretable visualizations. In particular, TopoMap maps the high-dimensional data into a visual space, guaranteeing that the 0-dimensional persistence diagram of the Rips filtration of the visual space matches the one from the high-dimensional data. However, the original TopoMap algorithm can be slow and its layout can be too sparse for large and complex datasets. In this paper, we propose three improvements to TopoMap: 1) a more space-efficient layout, 2) a significantly faster implementation, and 3) a novel TreeMap-based representation that makes use of the topological hierarchy to aid the exploration of the projections. These advancements make TopoMap, now referred to as TopoMap++, a more powerful tool for visualizing high-dimensional data which we demonstrate through different use case scenarios., Comment: This is the author's version of the article that has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics (TVCG)

We have investigated bound state solutions for a scalar particle described in the spacetime of a chiral cosmic string subjected to rotational effects in a Kaluza-Klein theory. We saw that combinations between the parameters that describe the generalized spacetime impose upper and lower limits for the radial coordinate leading to well-established boundary conditions.

In this paper, we theoretically investigate the deflection of light produced by a topologically charged Holonomy corrected Schwarzschild black hole. The study is carried out both in the weak field limit and in the strong field limit. We analytically deduced the expansions for light deflection in the two limits and, from them, we determined the observables in order to provide elements so that observational tools are able to identify these solutions. We model possible gravitational scenarios in order to verify the possible gravitational characteristics of the solution., Comment: arXiv admin note: text overlap with arXiv:2309.05106

In this research, we examine relativistic fermions within the rotating frame of negative curvature wormholes. Initially, as is typical in our context, we introduce the wormholes by embedding a curved surface into a higher dimensional flat Minkowski spacetime. Subsequently, we derive the spacetime metric that characterizes the rotating frame of these wormholes. We then investigate analytical solutions of the generalized Dirac equation within this framework. Through exploring a second-order nonperturbative wave equation, we seek exact solutions for fermions within the rotating frame of hyperbolic and elliptic wormholes, also known as negative curvature wormholes. Our analysis provides closed-form energy expressions, and we generalize our findings to Weyl fermions. By considering the impact of the rotating frame and curvature radius of wormholes, we discuss how these factors affect the evolution of fermionic fields, offering valuable insights into their behavior.

Iterative Bleaching Extends multipleXity (IBEX) is a versatile method for highly multiplexed imaging of diverse tissues. Based on open science principles, we created the IBEX Knowledge-Base, a resource for reagents, protocols and more, to empower innovation., Comment: 8 pages, 1 figure, 9 references

With the growing advances in deep learning based technologies the detection and identification of co-occurring objects is a challenging task which has many applications in areas such as, security and surveillance. In this paper, we propose a novel framework called SDLNet- Statistical analysis with Deep Learning Network that identifies co-occurring objects in conjunction with base objects in multilabel object categories. The pipeline of proposed work is implemented in two stages: in the first stage of SDLNet we deal with multilabel detectors for discovering labels, and in the second stage we perform co-occurrence matrix analysis. In co-occurrence matrix analysis, we learn co-occurrence statistics by setting base classes and frequently occurring classes, following this we build association rules and generate frequent patterns. The crucial part of SDLNet is recognizing base classes and making consideration for co-occurring classes. Finally, the generated co-occurrence matrix based on frequent patterns will show base classes and their corresponding co-occurring classes. SDLNet is evaluated on two publicly available datasets: Pascal VOC and MS-COCO. The experimental results on these benchmark datasets are reported in Sec 4., Comment: 8 pages, 3 figures, ICMLT-2024. arXiv admin note: text overlap with arXiv:2403.17223

With the increasing use of black-box Machine Learning (ML) techniques in critical applications, there is a growing demand for methods that can provide transparency and accountability for model predictions. As a result, a large number of local explainability methods for black-box models have been developed and popularized. However, machine learning explanations are still hard to evaluate and compare due to the high dimensionality, heterogeneous representations, varying scales, and stochastic nature of some of these methods. Topological Data Analysis (TDA) can be an effective method in this domain since it can be used to transform attributions into uniform graph representations, providing a common ground for comparison across different explanation methods. We present a novel topology-driven visual analytics tool, Mountaineer, that allows ML practitioners to interactively analyze and compare these representations by linking the topological graphs back to the original data distribution, model predictions, and feature attributions. Mountaineer facilitates rapid and iterative exploration of ML explanations, enabling experts to gain deeper insights into the explanation techniques, understand the underlying data distributions, and thus reach well-founded conclusions about model behavior. Furthermore, we demonstrate the utility of Mountaineer through two case studies using real-world data. In the first, we show how Mountaineer enabled us to compare black-box ML explanations and discern regions of and causes of disagreements between different explanations. In the second, we demonstrate how the tool can be used to compare and understand ML models themselves. Finally, we conducted interviews with three industry experts to help us evaluate our work., Comment: Author version of article accepted to IEEE Transactions on Visualization and Computer Graphics

A possible route to test whether macroscopic systems can acquire quantum features using superconducting circuits is here presented. It is shown that under general assumptions a classical test current pulse of fixed energy and adjustable length acquires quantum features after interacting with the quantum vacuum of the photon field. Further, it is shown that the mere existence of vacuum fluctuations can lead to the breakdown of energy and momentum conservation, and as the length of the pulse grows with respect to the characteristic size of the quantum system, the test pulse undergoes quantum-to-classical transition. This model differs from previous ones for its simplicity and points towards a new way of creating correlated systems suitable for quantum-based technology.

Introduction and Objective. Assuming the need to rethink Education and its function as an element of social transformation and generator of a profound ethical change and promoter of other ways of being on planet Earth, this study seeks to understand the possible dialogues between Earth Pedagogy and Astrobiology in the construction and proposition of continuing education processes for science teachers. Methodology. A mini-course was promoted aimed at teachers and an attempt was made to report the construction process of the training process and, using Content Analysis, to analyze the contributions pointed out by the participants to their training and teaching practice. Results and Conclusion. The dialogue between knowledge in the continuing education of teachers contributes with fundamental elements to teaching practice, covering cognitive, emotional, individual and collective aspects, as well as highlighting the role of the university in promoting initiatives that bring Education and Complexity together., Comment: This article is written in Portuguese

In this paper, we propose a novel deep learning based approach for identifying co-occurring objects in conjunction with base objects in multilabel object categories. Nowadays, with the advancement in computer vision based techniques we need to know about co-occurring objects with respect to base object for various purposes. The pipeline of the proposed work is composed of two stages: in the first stage of the proposed model we detect all the bounding boxes present in the image and their corresponding labels, then in the second stage we perform co-occurrence matrix analysis. In co-occurrence matrix analysis, we set base classes based on the maximum occurrences of the labels and build association rules and generate frequent patterns. These frequent patterns will show base classes and their corresponding co-occurring classes. We performed our experiments on two publicly available datasets: Pascal VOC and MS-COCO. The experimental results on public benchmark dataset is reported in Sec 4. Further we extend this work by considering all frequently objects as unlabeled and what if they are occluded as well., Comment: 6 pages, 2 figures

Given a presilting object in a triangulated category, we find necessary and sufficient conditions for the existence of a complement. This is done both for classic (pre)silting objects and for large (pre)silting objects. The key technique is the study of associated co-t-structures. As a consequence of our techniques we recover some known cases of the existence of complements, including for derived categories of some hereditary abelian categories and for silting-discrete algebras. Moreover, we also show that a finite-dimensional algebra is silting discrete if and only if every bounded large silting complex is equivalent to a compact one., Comment: 25 pages, 1 figure. Comments are welcome!

We consider Klein-Gordon (KG) particles in a global monopole (GM) spacetime within Eddington-inspired Born-Infeld gravity (EiBI-gravity) and in a Wu-Yang magnetic monopole (WYMM). We discuss a set of KG-oscillators in such spacetime settings. We propose a textbook power series expansion for the KG radial wave function that allows us to retrieve the exact energy levels for KG-oscillators in a GM spacetime and a WYMM without EiBI-gravity. We, moreover, report some \textit{conditionally exact}, closed form, energy levels (through some parametric correlations) for KG-oscillators in a GM spacetime and a WYMM within EiBI-gravity, and for massless KG-oscillators in a GM spacetime and a WYMM within EiBI-gravity under the influence of a Coulomb plus linear Lorentz scalar potential. We study and discuss the effects of the Eddington parameter $\kappa$, GM-parameter $\alpha$, WYMM strength $\sigma$, KG-oscillators' frequency $\Omega$, and the coupling parameters of the Coulomb plus linear Lorentz scalar potential, on the spectroscopic structure of the KG-oscillators at hand. Such effects are studied over a vast range of the radial quantum number $n_r\geq 0$ and include energy levels clustering at $\kappa>>1$ (i.e., extreme EiBI-gravity), and at $|\sigma|>>1$ (i.e., extreme WYMM strength)., Comment: 17 pages, 8 figures

This paper presents the reproduction of two studies focused on the perception of micro and macro expressions of Virtual Humans (VHs) generated by Computer Graphics (CG), first described in 2014 and replicated in 2021. The 2014 study referred to a VH realistic, whereas, in 2021, it referred to a VH cartoon. In our work, we replicate the study by using a realistic CG character. Our main goals are to compare the perceptions of micro and macro expressions between levels of realism (2021 cartoon versus 2023 realistic) and between realistic characters in different periods (i.e., 2014 versus 2023). In one of our results, people more easily recognized micro expressions in realistic VHs than in a cartoon VH. In another result, we show that the participants' perception was similar for both micro and macro expressions in 2014 and 2023., Comment: Accepted on the 2023 Brazilian Symposium on Games and Digital Entertainment (SBGames 2023)

This paper explores the dynamics of the Klein-Gordon oscillator in the presence of a cosmic string in Som Raychaudhuri spacetime. The exact solutions for the free case and the oscillator case are obtained and discussed. These solutions reveal the effects of the cosmic string and the spacetime geometry on the bosonic particles. To illustrate these results, some figures and tables have been included.

The Burgers equation is a well-established test case in the computational modeling of several phenomena such as fluid dynamics, gas dynamics, shock theory, cosmology, and others. In this work, we present the application of Physics-Informed Neural Networks (PINNs) with an implicit Euler transfer learning approach to solve the Burgers equation. The proposed approach consists in seeking a time-discrete solution by a sequence of Artificial Neural Networks (ANNs). At each time step, the previous ANN transfers its knowledge to the next network model, which learns the current time solution by minimizing a loss function based on the implicit Euler approximation of the Burgers equation. The approach is tested for two benchmark problems: the first with an exact solution and the other with an alternative analytical solution. In comparison to the usual PINN models, the proposed approach has the advantage of requiring smaller neural network architectures with similar accurate results and potentially decreasing computational costs., Comment: 11 pages, 3 figures, conference paper XXVI ENMC/XIV ECTM 2023, Nova Friburgo, Brazil

The representation theory of a commutative noetherian ring is tightly controlled by its prime spectrum. In this article we use the prime spectrum to describe mutation of cosilting objects in the derived category of a commutative noetherian ring., Comment: Submitted to the proceedings of the International Conference on Representations of Algebras (ICRA) 2022; Comments are welcome!

In the present work, we theoretically investigate gravitational lensing in the spacetime of a holonomy corrected Schwarzschild black hole. Analytical expressions for the light deflection angle are obtained in both the weak field limit and the strong field limit. Furthermore, we analyze observables, such as relativistic images and magnifications, and compare the results with those expected in a Schwarzschild spacetime. We discuss the possibilities and difficulties of investigating such a solution in practice.

In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp images. For this reason, approaches that explicitly use a forward degradation model received significantly less attention. However, a well-defined specification of the blur genesis, as an intermediate step, promotes the generalization and explainability of the method. Towards this goal, we propose a learning-based motion deblurring method based on dense non-uniform motion blur estimation followed by a non-blind deconvolution approach. Specifically, given a blurry image, a first network estimates the dense per-pixel motion blur kernels using a lightweight representation composed of a set of image-adaptive basis motion kernels and the corresponding mixing coefficients. Then, a second network trained jointly with the first one, unrolls a non-blind deconvolution method using the motion kernel field estimated by the first network. The model-driven aspect is further promoted by training the networks on sharp/blurry pairs synthesized according to a convolution-based, non-uniform motion blur degradation model. Qualitative and quantitative evaluation shows that the kernel prediction network produces accurate motion blur estimates, and that the deblurring pipeline leads to restorations of real blurred images that are competitive or superior to those obtained with existing end-to-end deep learning-based methods. Code and trained models are available at https://github.com/GuillermoCarbajal/J-MKPD/.

Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the theory of identifiability. The identifiability of independent latent factors is proven to be impossible in the unsupervised i.i.d. setting under a general nonlinear map from factors to observations. In this work, however, we demonstrate that it is possible to recover quantized latent factors under a generic nonlinear diffeomorphism. We only assume that the latent factors have independent discontinuities in their density, without requiring the factors to be statistically independent. We introduce this novel form of identifiability, termed quantized factor identifiability, and provide a comprehensive proof of the recovery of the quantized factors., Comment: Appears in: 3rd Conference on Causal Learning and Reasoning (CLeaR 2024). 39 pages

In the present paper, we study several aspects of gravitational lensing caused by a topologically charged Monopole/Wormhole, both in the weak field limit and in the strong field limit. We calculate the light deflection and then use it to determine the observables, with which one can investigate the existence of these objects through observational tools. We emphasize that the presence of the topological charge produces changes in the observables in relation to the case of General Relativity Ellis-Bronnikov wormhole.

This research investigates the relativistic quantum dynamics of spin-0 scalar massive charged particles via the relativistic Feshbach-Villars oscillator in the background of the Kaluza-Klein Theory. We solve the Feshbach-Villars equation in the abckground of a cosmic string spec-time in the context of the Kaluza-Klein and presented the eigenvalue solution. Afterward, we rewrite this system in the case of the Feshbach-Villars quantum oscillator and obtain the eigenvalue analytically. Finally, we study the interaction of the Feshbach-Villars equation and oscillator in a cosmic dislocation in the Som-Raychaudhuri in the context of the Kaluza-Klein Theory and solve the wave equation analytically. We analyze the influence of topological defect in the quantification of energy and wave function of the Feshbach-Villars oscillator and with the external fields in the last one, Comment: arXiv admin note: text overlap with arXiv:2304.12496 (Accepted for publication In Nuclear Physics B)

The quantum cosmology of the flat Friedmann-Lema{\^i}tre-Robertson-Walker Universe, filled with a scalar field, is considered in the de Broglie-Bohm (dBB) interpretation framework. A stiff-matter quantum bounce solution is obtained. The bouncing and subsequent pre-inflationary and inflationary dynamics are studied in details. We consider some representative primordial inflation models as examples, for which analytical expressions characterizing the dynamical quantities can be explicitly derived. The dependence of the inflationary dynamics on the quantum bounce parameters is then analyzed. The parameters emerging from our description are constrained by requiring the produced dynamics to be in accordance with some key cosmological quantities. The constraining conditions are also illustrated through regions of parameter space in terms of the bounce quantities., Comment: Replaced with version matching the published one